Ultrasonic signal denoising based on autoencoder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At present, denoising parameters in different signal processing algorithms require a specific signal waveform to be set. Human factors would significantly affect the denoising result. To solve this problem, we proposed a signal adaptive denoising method based on a denoising autoencoder to achieve denoising on ultrasonic signals. By applying this method to sample signals and comparing with the singular value decomposition (SVD), principal component analysis (PCA), and wavelet algorithms, it is found that this method can effectively suppress the noise at different noise intensities. Using the signal to noise ratio, root mean square error, and autocorrelation coefficient as evaluation parameters in the experiment, the overall denoising effect of the proposed method is better than that of PCA, and this method is better than the wavelet and SVD algorithms having a relatively weak noise intensity. In addition, by comparing the reconstructed signal curve of the proposed method and that of the wavelet algorithm, the proposed method can retain the information of signal saltation with a better performance. Finally, we apply this method for processing ultrasonic signals and verify its effectiveness from time and frequency domain diagrams.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it